Hi, I have been doing some work with 0.4.7 lately and am wondering what the exact strategy/formula behind the .getSensitivityAnalyzer() is for dealing with SVM weights. My original intuition was that this function was a shortcut to grab the hyperplane weights and that calling it with a dataset again after it had already been trained was a formality (a silly assumption, but based on the ValueError I was getting when not providing a dataset). My code looks similar to this (I know there are more convenient ways to do sample partitioning, this was set up for easy access to training and testing predictions):
dset=normalFeatureDataset() clf = SVM(C=1) training_set = dset.selectSamples(dset.chunks != 0) clf.train(training_set) sens = clf.getSensitivityAnalyzer() predictions = clf.predict(dset.samples) # see how it does on training and testing weights = sens(dset) # turns out that this actually retrains clf I was confused to see that the weights were identical no matter which fold was left out. Should I actually be using weights = sens(training_set) or is there some way to get the weights without calling with a dataset? lots of related questions: What would I need to pull out of a linearSVM to reproduce its predictions? Is there an analog of getSensitivityAnalyzer() that would give me the w and b from the SVM's wx+b? What is the difference between w and what is returned by getSensitivityAnalyzer()? Thanks as always, Matt
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